About this Course

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Learner Career Outcomes

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started a new career after completing these courses

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got a tangible career benefit from this course

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 25 hours to complete

English

Subtitles: English, Korean, Arabic

Skills you will gain

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree

Learner Career Outcomes

37%

started a new career after completing these courses

38%

got a tangible career benefit from this course

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 25 hours to complete

English

Subtitles: English, Korean, Arabic

Offered by

University of Washington logo

University of Washington

Syllabus - What you will learn from this course

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Week
1

Week 1

1 hour to complete

Welcome

1 hour to complete
4 videos (Total 25 min), 4 readings
4 videos
Course overview3m
Module-by-module topics covered8m
Assumed background6m
4 readings
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Software tools you'll need for this course10m
A big week ahead!10m
Week
2

Week 2

4 hours to complete

Nearest Neighbor Search

4 hours to complete
22 videos (Total 137 min), 4 readings, 5 quizzes
22 videos
1-NN algorithm2m
k-NN algorithm6m
Document representation5m
Distance metrics: Euclidean and scaled Euclidean6m
Writing (scaled) Euclidean distance using (weighted) inner products4m
Distance metrics: Cosine similarity9m
To normalize or not and other distance considerations6m
Complexity of brute force search1m
KD-tree representation9m
NN search with KD-trees7m
Complexity of NN search with KD-trees5m
Visualizing scaling behavior of KD-trees4m
Approximate k-NN search using KD-trees7m
Limitations of KD-trees3m
LSH as an alternative to KD-trees4m
Using random lines to partition points5m
Defining more bins3m
Searching neighboring bins8m
LSH in higher dimensions4m
(OPTIONAL) Improving efficiency through multiple tables22m
A brief recap2m
4 readings
Slides presented in this module10m
Choosing features and metrics for nearest neighbor search10m
(OPTIONAL) A worked-out example for KD-trees10m
Implementing Locality Sensitive Hashing from scratch10m
5 practice exercises
Representations and metrics12m
Choosing features and metrics for nearest neighbor search10m
KD-trees10m
Locality Sensitive Hashing10m
Implementing Locality Sensitive Hashing from scratch10m
Week
3

Week 3

2 hours to complete

Clustering with k-means

2 hours to complete
13 videos (Total 79 min), 2 readings, 3 quizzes
13 videos
An unsupervised task6m
Hope for unsupervised learning, and some challenge cases4m
The k-means algorithm7m
k-means as coordinate descent6m
Smart initialization via k-means++4m
Assessing the quality and choosing the number of clusters9m
Motivating MapReduce8m
The general MapReduce abstraction5m
MapReduce execution overview and combiners6m
MapReduce for k-means7m
Other applications of clustering7m
A brief recap1m
2 readings
Slides presented in this module10m
Clustering text data with k-means10m
3 practice exercises
k-means18m
Clustering text data with K-means16m
MapReduce for k-means10m
Week
4

Week 4

3 hours to complete

Mixture Models

3 hours to complete
15 videos (Total 91 min), 4 readings, 3 quizzes
15 videos
Aggregating over unknown classes in an image dataset6m
Univariate Gaussian distributions2m
Bivariate and multivariate Gaussians7m
Mixture of Gaussians6m
Interpreting the mixture of Gaussian terms5m
Scaling mixtures of Gaussians for document clustering5m
Computing soft assignments from known cluster parameters7m
(OPTIONAL) Responsibilities as Bayes' rule5m
Estimating cluster parameters from known cluster assignments6m
Estimating cluster parameters from soft assignments8m
EM iterates in equations and pictures6m
Convergence, initialization, and overfitting of EM9m
Relationship to k-means3m
A brief recap1m
4 readings
Slides presented in this module10m
(OPTIONAL) A worked-out example for EM10m
Implementing EM for Gaussian mixtures10m
Clustering text data with Gaussian mixtures10m
3 practice exercises
EM for Gaussian mixtures18m
Implementing EM for Gaussian mixtures12m
Clustering text data with Gaussian mixtures8m

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About the Machine Learning Specialization

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
Machine Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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